Too much text to read manually
Feedback, tickets or reports arrive faster than teams can review them.
04 / Hybrid Intelligence
Natural Language Processing
Emails, tickets, reports, reviews, transcripts and records hold information organisations rarely use fully, because reading them at scale is impractical. Natural language processing makes that text searchable, sortable and measurable. We apply the right technique for each task, from compact classifiers to large language models.
At a glance
- Service
- Natural Language Processing
- Pillar
- Hybrid Intelligence
- Typical starting point
- Text data and task assessment
- Typical first phase
- 6 to 10 weeks for a first NLP solution
- Key technologies
- Natural Language Processing, Large Language Models, Machine Learning
Durations depend on scope, data readiness and integration needs.
What is NLP?
Short answer
Natural language processing (NLP) is a field of artificial intelligence concerned with enabling computers to understand, analyse and generate human language. Common NLP tasks include text classification, named entity recognition, information extraction, sentiment analysis, semantic search, summarisation, translation and speech transcription analysis.
Large language models can perform many NLP tasks with little training data, which has made language AI far more accessible. They are not always the best choice, though. For high-volume classification or extraction, smaller fine-tuned models are often faster, cheaper and easier to control, while LLMs excel at complex or varied tasks.
We choose techniques by task, volume, accuracy requirement, latency, cost and data sensitivity. India's linguistic diversity adds another dimension: mixed-language text, transliteration and regional scripts need specific attention and testing.
Business problems
What problems does NLP solve?
These are the situations where organisations most often find this service worthwhile.
Slow routing of requests
Emails and tickets wait in shared queues until someone reads and assigns them.
Poor internal search
Keyword search fails to find relevant documents when different words are used.
Missed signals in customer feedback
Emerging complaints or risks are noticed late.
Manual extraction from text
Names, dates, amounts and obligations are copied from text by hand.
Multilingual content
Content in several languages or mixed scripts cannot be analysed consistently.
Use cases
Where can NLP be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Customer experience | Classifying feedback and complaints by topic and sentiment to spot trends. |
| Service operations | Automatic ticket categorisation, priority detection and routing. |
| Knowledge management | Semantic search across documents, wikis and archives. |
| Compliance and risk | Monitoring communications or documents for specific risk indicators, with human review. |
| Public grievance handling | Categorising and routing citizen grievances in multiple languages. |
| Contact centres | Analysing call transcripts for topics, compliance phrases and quality. |
Who should consider it
- Organisations receiving large volumes of text
- Customer experience and service teams
- Public bodies handling multilingual communications
- Knowledge-intensive organisations with large archives
How AARHIT approaches it
How we deliver NLP
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Task and data assessment
We define the NLP tasks, examine text samples for language, quality and variety, and set accuracy goals.
Output: task definitions and data assessment.
Stage 2 of 6
Annotation and test sets
Representative samples are labelled with clear guidelines to create training and evaluation data.
Output: labelled datasets.
Stage 3 of 6
Approach selection
We compare rules, fine-tuned transformer models and LLM approaches on accuracy, speed and cost.
Output: comparison results and recommended approach.
Stage 4 of 6
Model development
The selected approach is developed and tuned, including language-specific handling where needed.
Output: validated NLP models.
Stage 5 of 6
Integration
Models are integrated into ticketing, CRM, search or analytics systems with confidence handling.
Output: integrated solution.
Stage 6 of 6
Monitoring and refinement
Accuracy is monitored on sampled production data, and new categories or language patterns are incorporated.
Output: monitoring dashboards and update process.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Models | BERT-family and multilingual transformers, sentence embeddings, LLMs |
| Indian languages | IndicBERT, MuRIL, AI4Bharat resources, transliteration tools |
| Frameworks | Hugging Face Transformers, spaCy, scikit-learn |
| Search | Elasticsearch or OpenSearch, vector search, hybrid retrieval, re-ranking |
| Speech | Speech-to-text for transcript analysis |
| Integration | APIs, streaming pipelines, connectors to service and CRM platforms |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Faster handling and routing of text-based requests
- Visibility of trends in feedback and communications
- Search that finds relevant information by meaning
- Structured data extracted from unstructured text
- Consistent analysis across languages
How long does implementation generally take?
Typical timeline
A first NLP solution for a defined task such as ticket classification or feedback analysis typically takes 6 to 10 weeks, including annotation and integration. Multilingual or multi-task solutions take longer.
What determines complexity?
- Number of categories or entities
- Language variety and code-mixing
- Volume and speed requirements
- Availability of labelled examples
- Integration targets
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Define categories with users
Categories that make sense to analysts but not to operational teams lead to low adoption.
Expect category change
New topics emerge. Plan how categories and models will be updated.
Handle informal language
Customer text includes slang, abbreviations and errors that formal models may not expect.
| Risk to consider | How we manage it |
|---|---|
| Misclassification of important items | Confidence thresholds, priority rules and review of high-risk categories. |
| Bias across languages or groups | Evaluation broken down by language and segment. |
| Sensitive information in text | PII detection, masking and access controls. |
| Model drift as language changes | Monitoring, periodic labelling and retraining. |
FAQ
Questions about NLP
What is natural language processing used for in business?
Common uses include classifying and routing emails and tickets, analysing customer feedback, extracting information from text, semantic search over documents, summarising content and analysing call transcripts.
Are large language models replacing traditional NLP?
LLMs handle many NLP tasks well with little training data, but compact fine-tuned models are often better for high-volume, well-defined tasks due to speed, cost and control. Many solutions use both.
Can NLP handle Hindi, Hinglish and regional languages?
Yes, with suitable models and testing. Multilingual and India-focused models support many Indian languages, and code-mixed text such as Hinglish needs specific evaluation.
How much labelled data is needed?
For classification with fine-tuned models, a few hundred examples per category is often a reasonable start. LLM-based approaches can work with fewer examples but still need test data to measure accuracy.
What is semantic search?
Semantic search finds information based on meaning rather than exact keywords, using embeddings that represent text as vectors, so a search can match documents that use different wording for the same idea.
Next step
Considering NLP for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.